Overview

Dataset statistics

Number of variables44
Number of observations125972
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.3 MiB
Average record size in memory352.0 B

Variable types

Numeric29
Categorical15

Alerts

num_outbound_cmds has constant value "0"Constant
service has a high cardinality: 70 distinct valuesHigh cardinality
src_bytes is highly overall correlated with dst_bytes and 10 other fieldsHigh correlation
dst_bytes is highly overall correlated with src_bytes and 8 other fieldsHigh correlation
hot is highly overall correlated with num_compromised and 1 other fieldsHigh correlation
num_compromised is highly overall correlated with hotHigh correlation
count is highly overall correlated with src_bytes and 11 other fieldsHigh correlation
srv_count is highly overall correlated with count and 1 other fieldsHigh correlation
serror_rate is highly overall correlated with src_bytes and 9 other fieldsHigh correlation
srv_serror_rate is highly overall correlated with src_bytes and 8 other fieldsHigh correlation
rerror_rate is highly overall correlated with srv_rerror_rate and 2 other fieldsHigh correlation
srv_rerror_rate is highly overall correlated with rerror_rate and 2 other fieldsHigh correlation
same_srv_rate is highly overall correlated with src_bytes and 13 other fieldsHigh correlation
diff_srv_rate is highly overall correlated with src_bytes and 11 other fieldsHigh correlation
dst_host_count is highly overall correlated with count and 5 other fieldsHigh correlation
dst_host_srv_count is highly overall correlated with src_bytes and 8 other fieldsHigh correlation
dst_host_same_srv_rate is highly overall correlated with src_bytes and 12 other fieldsHigh correlation
dst_host_diff_srv_rate is highly overall correlated with src_bytes and 6 other fieldsHigh correlation
dst_host_same_src_port_rate is highly overall correlated with count and 3 other fieldsHigh correlation
dst_host_srv_diff_host_rate is highly overall correlated with count and 3 other fieldsHigh correlation
dst_host_serror_rate is highly overall correlated with src_bytes and 10 other fieldsHigh correlation
dst_host_srv_serror_rate is highly overall correlated with src_bytes and 7 other fieldsHigh correlation
dst_host_rerror_rate is highly overall correlated with rerror_rate and 2 other fieldsHigh correlation
dst_host_srv_rerror_rate is highly overall correlated with rerror_rate and 2 other fieldsHigh correlation
protocol_type is highly overall correlated with srv_count and 2 other fieldsHigh correlation
service is highly overall correlated with protocol_type and 3 other fieldsHigh correlation
flag is highly overall correlated with logged_inHigh correlation
land is highly overall correlated with attackHigh correlation
wrong_fragment is highly overall correlated with attackHigh correlation
logged_in is highly overall correlated with count and 7 other fieldsHigh correlation
root_shell is highly overall correlated with su_attemptedHigh correlation
su_attempted is highly overall correlated with root_shellHigh correlation
is_guest_login is highly overall correlated with hot and 1 other fieldsHigh correlation
attack is highly overall correlated with protocol_type and 4 other fieldsHigh correlation
attack_class is highly overall correlated with service and 2 other fieldsHigh correlation
flag is highly imbalanced (55.8%)Imbalance
land is highly imbalanced (99.7%)Imbalance
wrong_fragment is highly imbalanced (95.1%)Imbalance
urgent is highly imbalanced (99.9%)Imbalance
root_shell is highly imbalanced (98.5%)Imbalance
su_attempted is highly imbalanced (99.5%)Imbalance
num_shells is highly imbalanced (99.7%)Imbalance
is_host_login is highly imbalanced (> 99.9%)Imbalance
is_guest_login is highly imbalanced (92.3%)Imbalance
attack is highly imbalanced (60.0%)Imbalance
src_bytes is highly skewed (γ1 = 190.6685901)Skewed
dst_bytes is highly skewed (γ1 = 290.0517595)Skewed
num_failed_logins is highly skewed (γ1 = 53.76421073)Skewed
num_compromised is highly skewed (γ1 = 250.1068908)Skewed
num_root is highly skewed (γ1 = 236.9127838)Skewed
num_file_creations is highly skewed (γ1 = 55.66511972)Skewed
num_access_files is highly skewed (γ1 = 45.55478025)Skewed
duration has 115954 (92.0%) zerosZeros
src_bytes has 49392 (39.2%) zerosZeros
dst_bytes has 67966 (54.0%) zerosZeros
hot has 123301 (97.9%) zerosZeros
num_failed_logins has 125850 (99.9%) zerosZeros
num_compromised has 124686 (99.0%) zerosZeros
num_root has 125323 (99.5%) zerosZeros
num_file_creations has 125685 (99.8%) zerosZeros
num_access_files has 125601 (99.7%) zerosZeros
serror_rate has 86828 (68.9%) zerosZeros
srv_serror_rate has 88753 (70.5%) zerosZeros
rerror_rate has 109782 (87.1%) zerosZeros
srv_rerror_rate has 109766 (87.1%) zerosZeros
same_srv_rate has 2766 (2.2%) zerosZeros
diff_srv_rate has 76216 (60.5%) zerosZeros
srv_diff_host_rate has 97573 (77.5%) zerosZeros
dst_host_same_srv_rate has 6927 (5.5%) zerosZeros
dst_host_diff_srv_rate has 46989 (37.3%) zerosZeros
dst_host_same_src_port_rate has 63023 (50.0%) zerosZeros
dst_host_srv_diff_host_rate has 86903 (69.0%) zerosZeros
dst_host_serror_rate has 81385 (64.6%) zerosZeros
dst_host_srv_serror_rate has 85359 (67.8%) zerosZeros
dst_host_rerror_rate has 103178 (81.9%) zerosZeros
dst_host_srv_rerror_rate has 106615 (84.6%) zerosZeros

Reproduction

Analysis started2023-06-14 04:51:10.782937
Analysis finished2023-06-14 04:55:12.401876
Duration4 minutes and 1.62 second
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

duration
Real number (ℝ)

Distinct2981
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean287.14693
Minimum0
Maximum42908
Zeros115954
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:12.633945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum42908
Range42908
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2604.5255
Coefficient of variation (CV)9.0703583
Kurtosis156.07554
Mean287.14693
Median Absolute Deviation (MAD)0
Skewness11.880182
Sum36172473
Variance6783553.2
MonotonicityNot monotonic
2023-06-14T10:25:12.881660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115954
92.0%
1 1989
 
1.6%
2 843
 
0.7%
3 557
 
0.4%
4 351
 
0.3%
5 298
 
0.2%
27 197
 
0.2%
6 193
 
0.2%
28 181
 
0.1%
7 127
 
0.1%
Other values (2971) 5282
 
4.2%
ValueCountFrequency (%)
0 115954
92.0%
1 1989
 
1.6%
2 843
 
0.7%
3 557
 
0.4%
4 351
 
0.3%
5 298
 
0.2%
6 193
 
0.2%
7 127
 
0.1%
8 98
 
0.1%
9 95
 
0.1%
ValueCountFrequency (%)
42908 1
< 0.1%
42888 1
< 0.1%
42862 1
< 0.1%
42837 1
< 0.1%
42804 1
< 0.1%
42778 1
< 0.1%
42746 1
< 0.1%
42723 1
< 0.1%
42699 1
< 0.1%
42679 1
< 0.1%

protocol_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
tcp
102688 
udp
14993 
icmp
 
8291

Length

Max length4
Median length3
Mean length3.0658162
Min length3

Characters and Unicode

Total characters386207
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowudp
2nd rowtcp
3rd rowtcp
4th rowtcp
5th rowtcp

Common Values

ValueCountFrequency (%)
tcp 102688
81.5%
udp 14993
 
11.9%
icmp 8291
 
6.6%

Length

2023-06-14T10:25:13.139485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-14T10:25:13.506086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
tcp 102688
81.5%
udp 14993
 
11.9%
icmp 8291
 
6.6%

Most occurring characters

ValueCountFrequency (%)
p 125972
32.6%
c 110979
28.7%
t 102688
26.6%
u 14993
 
3.9%
d 14993
 
3.9%
i 8291
 
2.1%
m 8291
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 386207
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 125972
32.6%
c 110979
28.7%
t 102688
26.6%
u 14993
 
3.9%
d 14993
 
3.9%
i 8291
 
2.1%
m 8291
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 386207
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 125972
32.6%
c 110979
28.7%
t 102688
26.6%
u 14993
 
3.9%
d 14993
 
3.9%
i 8291
 
2.1%
m 8291
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 386207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 125972
32.6%
c 110979
28.7%
t 102688
26.6%
u 14993
 
3.9%
d 14993
 
3.9%
i 8291
 
2.1%
m 8291
 
2.1%

service
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct70
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
http
40338 
private
21853 
domain_u
9043 
smtp
7313 
ftp_data
6859 
Other values (65)
40566 

Length

Max length11
Median length10
Mean length5.466429
Min length3

Characters and Unicode

Total characters688617
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowother
2nd rowprivate
3rd rowhttp
4th rowhttp
5th rowprivate

Common Values

ValueCountFrequency (%)
http 40338
32.0%
private 21853
17.3%
domain_u 9043
 
7.2%
smtp 7313
 
5.8%
ftp_data 6859
 
5.4%
eco_i 4586
 
3.6%
other 4359
 
3.5%
ecr_i 3077
 
2.4%
telnet 2353
 
1.9%
finger 1767
 
1.4%
Other values (60) 24424
19.4%

Length

2023-06-14T10:25:13.821397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
http 40338
32.0%
private 21853
17.3%
domain_u 9043
 
7.2%
smtp 7313
 
5.8%
ftp_data 6859
 
5.4%
eco_i 4586
 
3.6%
other 4359
 
3.5%
ecr_i 3077
 
2.4%
telnet 2353
 
1.9%
finger 1767
 
1.4%
Other values (60) 24424
19.4%

Most occurring characters

ValueCountFrequency (%)
t 145595
21.1%
p 88150
12.8%
a 51382
 
7.5%
h 49666
 
7.2%
e 49119
 
7.1%
i 48525
 
7.0%
r 34885
 
5.1%
_ 29464
 
4.3%
o 24559
 
3.6%
n 22585
 
3.3%
Other values (30) 144687
21.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 651472
94.6%
Connector Punctuation 29464
 
4.3%
Decimal Number 6185
 
0.9%
Uppercase Letter 1496
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 145595
22.3%
p 88150
13.5%
a 51382
 
7.9%
h 49666
 
7.6%
e 49119
 
7.5%
i 48525
 
7.4%
r 34885
 
5.4%
o 24559
 
3.8%
n 22585
 
3.5%
v 22472
 
3.4%
Other values (15) 114534
17.6%
Decimal Number
ValueCountFrequency (%)
4 1708
27.6%
3 1656
26.8%
0 866
14.0%
5 862
13.9%
9 862
13.9%
1 148
 
2.4%
2 79
 
1.3%
8 3
 
< 0.1%
7 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
Z 862
57.6%
C 187
 
12.5%
I 187
 
12.5%
R 187
 
12.5%
X 73
 
4.9%
Connector Punctuation
ValueCountFrequency (%)
_ 29464
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 652968
94.8%
Common 35649
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 145595
22.3%
p 88150
13.5%
a 51382
 
7.9%
h 49666
 
7.6%
e 49119
 
7.5%
i 48525
 
7.4%
r 34885
 
5.3%
o 24559
 
3.8%
n 22585
 
3.5%
v 22472
 
3.4%
Other values (20) 116030
17.8%
Common
ValueCountFrequency (%)
_ 29464
82.7%
4 1708
 
4.8%
3 1656
 
4.6%
0 866
 
2.4%
5 862
 
2.4%
9 862
 
2.4%
1 148
 
0.4%
2 79
 
0.2%
8 3
 
< 0.1%
7 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 688617
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 145595
21.1%
p 88150
12.8%
a 51382
 
7.5%
h 49666
 
7.2%
e 49119
 
7.1%
i 48525
 
7.0%
r 34885
 
5.1%
_ 29464
 
4.3%
o 24559
 
3.6%
n 22585
 
3.3%
Other values (30) 144687
21.0%

flag
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
SF
74944 
S0
34851 
REJ
11233 
RSTR
 
2421
RSTO
 
1562
Other values (6)
 
961

Length

Max length6
Median length2
Mean length2.1560426
Min length2

Characters and Unicode

Total characters271601
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSF
2nd rowS0
3rd rowSF
4th rowSF
5th rowREJ

Common Values

ValueCountFrequency (%)
SF 74944
59.5%
S0 34851
27.7%
REJ 11233
 
8.9%
RSTR 2421
 
1.9%
RSTO 1562
 
1.2%
S1 365
 
0.3%
SH 271
 
0.2%
S2 127
 
0.1%
RSTOS0 103
 
0.1%
S3 49
 
< 0.1%

Length

2023-06-14T10:25:14.196391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sf 74944
59.5%
s0 34851
27.7%
rej 11233
 
8.9%
rstr 2421
 
1.9%
rsto 1562
 
1.2%
s1 365
 
0.3%
sh 271
 
0.2%
s2 127
 
0.1%
rstos0 103
 
0.1%
s3 49
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 114796
42.3%
F 74944
27.6%
0 34954
 
12.9%
R 17740
 
6.5%
E 11233
 
4.1%
J 11233
 
4.1%
T 4132
 
1.5%
O 1711
 
0.6%
1 365
 
0.1%
H 317
 
0.1%
Other values (2) 176
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 236106
86.9%
Decimal Number 35495
 
13.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 114796
48.6%
F 74944
31.7%
R 17740
 
7.5%
E 11233
 
4.8%
J 11233
 
4.8%
T 4132
 
1.8%
O 1711
 
0.7%
H 317
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 34954
98.5%
1 365
 
1.0%
2 127
 
0.4%
3 49
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 236106
86.9%
Common 35495
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 114796
48.6%
F 74944
31.7%
R 17740
 
7.5%
E 11233
 
4.8%
J 11233
 
4.8%
T 4132
 
1.8%
O 1711
 
0.7%
H 317
 
0.1%
Common
ValueCountFrequency (%)
0 34954
98.5%
1 365
 
1.0%
2 127
 
0.4%
3 49
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 271601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 114796
42.3%
F 74944
27.6%
0 34954
 
12.9%
R 17740
 
6.5%
E 11233
 
4.1%
J 11233
 
4.1%
T 4132
 
1.5%
O 1711
 
0.6%
1 365
 
0.1%
H 317
 
0.1%
Other values (2) 176
 
0.1%

src_bytes
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct3341
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45567.101
Minimum0
Maximum1.3799639 × 109
Zeros49392
Zeros (%)39.2%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:14.480521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median44
Q3276
95-th percentile1480
Maximum1.3799639 × 109
Range1.3799639 × 109
Interquartile range (IQR)276

Descriptive statistics

Standard deviation5870354.5
Coefficient of variation (CV)128.82879
Kurtosis39353.809
Mean45567.101
Median Absolute Deviation (MAD)44
Skewness190.66859
Sum5.7401788 × 109
Variance3.4461062 × 1013
MonotonicityNot monotonic
2023-06-14T10:25:14.838741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49392
39.2%
8 3691
 
2.9%
1 2432
 
1.9%
44 2334
 
1.9%
45 2089
 
1.7%
1032 2001
 
1.6%
46 1294
 
1.0%
43 1284
 
1.0%
105 998
 
0.8%
147 948
 
0.8%
Other values (3331) 59509
47.2%
ValueCountFrequency (%)
0 49392
39.2%
1 2432
 
1.9%
4 2
 
< 0.1%
5 28
 
< 0.1%
6 147
 
0.1%
7 107
 
0.1%
8 3691
 
2.9%
9 199
 
0.2%
10 195
 
0.2%
11 76
 
0.1%
ValueCountFrequency (%)
1379963888 1
< 0.1%
1167519497 1
< 0.1%
693375640 1
< 0.1%
621568663 1
< 0.1%
381709090 1
< 0.1%
217277339 1
< 0.1%
89581520 1
< 0.1%
24418776 1
< 0.1%
21945520 1
< 0.1%
18828976 1
< 0.1%

dst_bytes
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct9326
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19779.271
Minimum0
Maximum1.3099374 × 109
Zeros67966
Zeros (%)54.0%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:15.270829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3516
95-th percentile8314
Maximum1.3099374 × 109
Range1.3099374 × 109
Interquartile range (IQR)516

Descriptive statistics

Standard deviation4021285.1
Coefficient of variation (CV)203.30805
Kurtosis90941.013
Mean19779.271
Median Absolute Deviation (MAD)0
Skewness290.05176
Sum2.4916344 × 109
Variance1.6170734 × 1013
MonotonicityNot monotonic
2023-06-14T10:25:15.620016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67966
54.0%
105 1497
 
1.2%
8314 888
 
0.7%
330 528
 
0.4%
331 512
 
0.4%
44 511
 
0.4%
42 478
 
0.4%
328 470
 
0.4%
332 469
 
0.4%
4 454
 
0.4%
Other values (9316) 52199
41.4%
ValueCountFrequency (%)
0 67966
54.0%
1 22
 
< 0.1%
3 1
 
< 0.1%
4 454
 
0.4%
5 4
 
< 0.1%
6 1
 
< 0.1%
12 1
 
< 0.1%
14 1
 
< 0.1%
15 47
 
< 0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
1309937401 1
< 0.1%
400291060 2
< 0.1%
7028652 1
< 0.1%
5155468 1
< 0.1%
5153771 1
< 0.1%
5153460 1
< 0.1%
5151385 1
< 0.1%
5151154 1
< 0.1%
5151049 1
< 0.1%
5150938 1
< 0.1%

land
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125947 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125972
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 125947
> 99.9%
1 25
 
< 0.1%

Length

2023-06-14T10:25:15.929802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-14T10:25:16.153930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 125947
> 99.9%
1 25
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 125947
> 99.9%
1 25
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 125947
> 99.9%
1 25
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 125972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 125947
> 99.9%
1 25
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125947
> 99.9%
1 25
 
< 0.1%

wrong_fragment
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
124882 
3
 
884
1
 
206

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125972
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 124882
99.1%
3 884
 
0.7%
1 206
 
0.2%

Length

2023-06-14T10:25:16.402950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-14T10:25:16.677443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 124882
99.1%
3 884
 
0.7%
1 206
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 124882
99.1%
3 884
 
0.7%
1 206
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 124882
99.1%
3 884
 
0.7%
1 206
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 125972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 124882
99.1%
3 884
 
0.7%
1 206
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 124882
99.1%
3 884
 
0.7%
1 206
 
0.2%

urgent
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125963 
1
 
5
2
 
3
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125972
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 125963
> 99.9%
1 5
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%

Length

2023-06-14T10:25:16.919018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-14T10:25:17.236989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 125963
> 99.9%
1 5
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 125963
> 99.9%
1 5
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 125963
> 99.9%
1 5
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 125972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 125963
> 99.9%
1 5
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125963
> 99.9%
1 5
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%

hot
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2044105
Minimum0
Maximum77
Zeros123301
Zeros (%)97.9%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:17.426914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum77
Range77
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1499769
Coefficient of variation (CV)10.517937
Kurtosis168.01289
Mean0.2044105
Median Absolute Deviation (MAD)0
Skewness12.589835
Sum25750
Variance4.6224006
MonotonicityNot monotonic
2023-06-14T10:25:17.801271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 123301
97.9%
2 1037
 
0.8%
1 369
 
0.3%
28 277
 
0.2%
30 256
 
0.2%
4 173
 
0.1%
6 140
 
0.1%
5 76
 
0.1%
24 68
 
0.1%
19 57
 
< 0.1%
Other values (18) 218
 
0.2%
ValueCountFrequency (%)
0 123301
97.9%
1 369
 
0.3%
2 1037
 
0.8%
3 54
 
< 0.1%
4 173
 
0.1%
5 76
 
0.1%
6 140
 
0.1%
7 5
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
77 1
 
< 0.1%
44 2
 
< 0.1%
33 1
 
< 0.1%
30 256
0.2%
28 277
0.2%
25 2
 
< 0.1%
24 68
 
0.1%
22 55
 
< 0.1%
21 1
 
< 0.1%
20 9
 
< 0.1%

num_failed_logins
Real number (ℝ)

SKEWED  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0012224939
Minimum0
Maximum5
Zeros125850
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:18.084275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.045239318
Coefficient of variation (CV)37.005762
Kurtosis3869.0386
Mean0.0012224939
Median Absolute Deviation (MAD)0
Skewness53.764211
Sum154
Variance0.0020465959
MonotonicityNot monotonic
2023-06-14T10:25:18.328513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 125850
99.9%
1 104
 
0.1%
2 9
 
< 0.1%
3 5
 
< 0.1%
4 3
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 125850
99.9%
1 104
 
0.1%
2 9
 
< 0.1%
3 5
 
< 0.1%
4 3
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 3
 
< 0.1%
3 5
 
< 0.1%
2 9
 
< 0.1%
1 104
 
0.1%
0 125850
99.9%

logged_in
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
76120 
1
49852 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125972
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 76120
60.4%
1 49852
39.6%

Length

2023-06-14T10:25:18.609460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-14T10:25:18.909280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 76120
60.4%
1 49852
39.6%

Most occurring characters

ValueCountFrequency (%)
0 76120
60.4%
1 49852
39.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 76120
60.4%
1 49852
39.6%

Most occurring scripts

ValueCountFrequency (%)
Common 125972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 76120
60.4%
1 49852
39.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 76120
60.4%
1 49852
39.6%

num_compromised
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct88
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27925253
Minimum0
Maximum7479
Zeros124686
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:19.135861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7479
Range7479
Interquartile range (IQR)0

Descriptive statistics

Standard deviation23.942137
Coefficient of variation (CV)85.736509
Kurtosis75955.625
Mean0.27925253
Median Absolute Deviation (MAD)0
Skewness250.10689
Sum35178
Variance573.22594
MonotonicityNot monotonic
2023-06-14T10:25:19.459482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 124686
99.0%
1 976
 
0.8%
2 98
 
0.1%
4 40
 
< 0.1%
3 38
 
< 0.1%
6 19
 
< 0.1%
5 17
 
< 0.1%
7 5
 
< 0.1%
8 3
 
< 0.1%
9 3
 
< 0.1%
Other values (78) 87
 
0.1%
ValueCountFrequency (%)
0 124686
99.0%
1 976
 
0.8%
2 98
 
0.1%
3 38
 
< 0.1%
4 40
 
< 0.1%
5 17
 
< 0.1%
6 19
 
< 0.1%
7 5
 
< 0.1%
8 3
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
7479 1
< 0.1%
1739 1
< 0.1%
1043 1
< 0.1%
884 2
< 0.1%
809 1
< 0.1%
789 1
< 0.1%
767 1
< 0.1%
761 1
< 0.1%
756 1
< 0.1%
751 1
< 0.1%

root_shell
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125803 
1
 
169

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125972
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 125803
99.9%
1 169
 
0.1%

Length

2023-06-14T10:25:19.749503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-14T10:25:20.033940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 125803
99.9%
1 169
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 125803
99.9%
1 169
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 125803
99.9%
1 169
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 125972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 125803
99.9%
1 169
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125803
99.9%
1 169
 
0.1%

su_attempted
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125892 
2
 
59
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125972
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 125892
99.9%
2 59
 
< 0.1%
1 21
 
< 0.1%

Length

2023-06-14T10:25:20.282977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-14T10:25:20.615544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 125892
99.9%
2 59
 
< 0.1%
1 21
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 125892
99.9%
2 59
 
< 0.1%
1 21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 125892
99.9%
2 59
 
< 0.1%
1 21
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 125972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 125892
99.9%
2 59
 
< 0.1%
1 21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125892
99.9%
2 59
 
< 0.1%
1 21
 
< 0.1%

num_root
Real number (ℝ)

SKEWED  ZEROS 

Distinct82
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30219414
Minimum0
Maximum7468
Zeros125323
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:20.884078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7468
Range7468
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24.399715
Coefficient of variation (CV)80.741854
Kurtosis70069.653
Mean0.30219414
Median Absolute Deviation (MAD)0
Skewness236.91278
Sum38068
Variance595.34609
MonotonicityNot monotonic
2023-06-14T10:25:21.199685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 125323
99.5%
1 273
 
0.2%
9 121
 
0.1%
6 99
 
0.1%
2 33
 
< 0.1%
5 24
 
< 0.1%
4 12
 
< 0.1%
3 7
 
< 0.1%
7 2
 
< 0.1%
857 2
 
< 0.1%
Other values (72) 76
 
0.1%
ValueCountFrequency (%)
0 125323
99.5%
1 273
 
0.2%
2 33
 
< 0.1%
3 7
 
< 0.1%
4 12
 
< 0.1%
5 24
 
< 0.1%
6 99
 
0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 121
 
0.1%
ValueCountFrequency (%)
7468 1
< 0.1%
1743 1
< 0.1%
1045 1
< 0.1%
993 1
< 0.1%
975 1
< 0.1%
889 1
< 0.1%
867 1
< 0.1%
857 2
< 0.1%
849 1
< 0.1%
841 1
< 0.1%

num_file_creations
Real number (ℝ)

SKEWED  ZEROS 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012669482
Minimum0
Maximum43
Zeros125685
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:21.616318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum43
Range43
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.48393699
Coefficient of variation (CV)38.197062
Kurtosis3603.2831
Mean0.012669482
Median Absolute Deviation (MAD)0
Skewness55.66512
Sum1596
Variance0.23419501
MonotonicityNot monotonic
2023-06-14T10:25:21.949321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 125685
99.8%
1 151
 
0.1%
2 41
 
< 0.1%
4 13
 
< 0.1%
3 5
 
< 0.1%
8 5
 
< 0.1%
15 5
 
< 0.1%
10 5
 
< 0.1%
5 5
 
< 0.1%
17 5
 
< 0.1%
Other values (25) 52
 
< 0.1%
ValueCountFrequency (%)
0 125685
99.8%
1 151
 
0.1%
2 41
 
< 0.1%
3 5
 
< 0.1%
4 13
 
< 0.1%
5 5
 
< 0.1%
6 3
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
43 1
 
< 0.1%
40 3
< 0.1%
38 1
 
< 0.1%
36 1
 
< 0.1%
34 1
 
< 0.1%
33 1
 
< 0.1%
29 1
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
26 3
< 0.1%

num_shells
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125925 
1
 
42
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125972
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 125925
> 99.9%
1 42
 
< 0.1%
2 5
 
< 0.1%

Length

2023-06-14T10:25:22.190574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-14T10:25:22.490770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 125925
> 99.9%
1 42
 
< 0.1%
2 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 125925
> 99.9%
1 42
 
< 0.1%
2 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 125925
> 99.9%
1 42
 
< 0.1%
2 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 125972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 125925
> 99.9%
1 42
 
< 0.1%
2 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125925
> 99.9%
1 42
 
< 0.1%
2 5
 
< 0.1%

num_access_files
Real number (ℝ)

SKEWED  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0040961484
Minimum0
Maximum9
Zeros125601
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:22.761168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.099369949
Coefficient of variation (CV)24.259363
Kurtosis2862.7816
Mean0.0040961484
Median Absolute Deviation (MAD)0
Skewness45.55478
Sum516
Variance0.0098743869
MonotonicityNot monotonic
2023-06-14T10:25:22.953233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 125601
99.7%
1 313
 
0.2%
2 29
 
< 0.1%
3 8
 
< 0.1%
5 6
 
< 0.1%
4 5
 
< 0.1%
6 4
 
< 0.1%
8 3
 
< 0.1%
7 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 125601
99.7%
1 313
 
0.2%
2 29
 
< 0.1%
3 8
 
< 0.1%
4 5
 
< 0.1%
5 6
 
< 0.1%
6 4
 
< 0.1%
7 2
 
< 0.1%
8 3
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 3
 
< 0.1%
7 2
 
< 0.1%
6 4
 
< 0.1%
5 6
 
< 0.1%
4 5
 
< 0.1%
3 8
 
< 0.1%
2 29
 
< 0.1%
1 313
 
0.2%
0 125601
99.7%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125972 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125972
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 125972
100.0%

Length

2023-06-14T10:25:23.129277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-14T10:25:23.321309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 125972
100.0%

Most occurring characters

ValueCountFrequency (%)
0 125972
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 125972
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 125972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 125972
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125972
100.0%

is_host_login
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125971 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125972
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 125971
> 99.9%
1 1
 
< 0.1%

Length

2023-06-14T10:25:23.545389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-14T10:25:23.809455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 125971
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 125971
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 125971
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 125972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 125971
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125971
> 99.9%
1 1
 
< 0.1%

is_guest_login
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
124785 
1
 
1187

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125972
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 124785
99.1%
1 1187
 
0.9%

Length

2023-06-14T10:25:23.945558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-14T10:25:24.105533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 124785
99.1%
1 1187
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 124785
99.1%
1 1187
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 124785
99.1%
1 1187
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 125972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 124785
99.1%
1 1187
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 124785
99.1%
1 1187
 
0.9%

count
Real number (ℝ)

Distinct512
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.108207
Minimum0
Maximum511
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:24.273639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median14
Q3143
95-th percentile286
Maximum511
Range511
Interquartile range (IQR)141

Descriptive statistics

Standard deviation114.50883
Coefficient of variation (CV)1.3614466
Kurtosis2.0068812
Mean84.108207
Median Absolute Deviation (MAD)13
Skewness1.5142636
Sum10595279
Variance13112.272
MonotonicityNot monotonic
2023-06-14T10:25:24.506092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 27763
22.0%
2 9473
 
7.5%
3 3962
 
3.1%
4 3550
 
2.8%
5 2980
 
2.4%
6 2413
 
1.9%
7 2325
 
1.8%
8 1902
 
1.5%
9 1712
 
1.4%
10 1610
 
1.3%
Other values (502) 68282
54.2%
ValueCountFrequency (%)
0 13
 
< 0.1%
1 27763
22.0%
2 9473
 
7.5%
3 3962
 
3.1%
4 3550
 
2.8%
5 2980
 
2.4%
6 2413
 
1.9%
7 2325
 
1.8%
8 1902
 
1.5%
9 1712
 
1.4%
ValueCountFrequency (%)
511 1437
1.1%
510 307
 
0.2%
509 243
 
0.2%
508 31
 
< 0.1%
507 6
 
< 0.1%
506 3
 
< 0.1%
505 2
 
< 0.1%
504 3
 
< 0.1%
503 4
 
< 0.1%
502 5
 
< 0.1%

srv_count
Real number (ℝ)

Distinct509
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.738093
Minimum0
Maximum511
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:24.729681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median8
Q318
95-th percentile158
Maximum511
Range511
Interquartile range (IQR)16

Descriptive statistics

Standard deviation72.636092
Coefficient of variation (CV)2.6186405
Kurtosis24.244269
Mean27.738093
Median Absolute Deviation (MAD)7
Skewness4.6941422
Sum3494223
Variance5276.0018
MonotonicityNot monotonic
2023-06-14T10:25:25.018677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 25398
20.2%
2 12819
 
10.2%
3 6336
 
5.0%
4 5526
 
4.4%
5 4636
 
3.7%
6 4156
 
3.3%
7 3992
 
3.2%
8 3697
 
2.9%
9 3528
 
2.8%
11 3293
 
2.6%
Other values (499) 52591
41.7%
ValueCountFrequency (%)
0 13
 
< 0.1%
1 25398
20.2%
2 12819
10.2%
3 6336
 
5.0%
4 5526
 
4.4%
5 4636
 
3.7%
6 4156
 
3.3%
7 3992
 
3.2%
8 3697
 
2.9%
9 3528
 
2.8%
ValueCountFrequency (%)
511 1012
0.8%
510 160
 
0.1%
509 49
 
< 0.1%
508 11
 
< 0.1%
507 3
 
< 0.1%
503 1
 
< 0.1%
502 2
 
< 0.1%
501 1
 
< 0.1%
500 2
 
< 0.1%
499 2
 
< 0.1%

serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct89
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28448679
Minimum0
Maximum1
Zeros86828
Zeros (%)68.9%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:25.290762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.44645668
Coefficient of variation (CV)1.5693406
Kurtosis-1.0546278
Mean0.28448679
Median Absolute Deviation (MAD)0
Skewness0.96318822
Sum35837.37
Variance0.19932356
MonotonicityNot monotonic
2023-06-14T10:25:25.699262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 86828
68.9%
1 34439
 
27.3%
0.5 493
 
0.4%
0.33 321
 
0.3%
0.07 305
 
0.2%
0.06 298
 
0.2%
0.08 254
 
0.2%
0.99 250
 
0.2%
0.01 216
 
0.2%
0.25 208
 
0.2%
Other values (79) 2360
 
1.9%
ValueCountFrequency (%)
0 86828
68.9%
0.01 216
 
0.2%
0.02 84
 
0.1%
0.03 150
 
0.1%
0.04 131
 
0.1%
0.05 192
 
0.2%
0.06 298
 
0.2%
0.07 305
 
0.2%
0.08 254
 
0.2%
0.09 189
 
0.2%
ValueCountFrequency (%)
1 34439
27.3%
0.99 250
 
0.2%
0.98 64
 
0.1%
0.97 79
 
0.1%
0.96 41
 
< 0.1%
0.95 29
 
< 0.1%
0.94 25
 
< 0.1%
0.93 18
 
< 0.1%
0.92 15
 
< 0.1%
0.91 8
 
< 0.1%

srv_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28248762
Minimum0
Maximum1
Zeros88753
Zeros (%)70.5%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:25.915297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.44702356
Coefficient of variation (CV)1.5824537
Kurtosis-1.0443176
Mean0.28248762
Median Absolute Deviation (MAD)0
Skewness0.97058496
Sum35585.53
Variance0.19983007
MonotonicityNot monotonic
2023-06-14T10:25:26.163377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 88753
70.5%
1 34874
 
27.7%
0.5 432
 
0.3%
0.33 273
 
0.2%
0.25 233
 
0.2%
0.2 132
 
0.1%
0.17 114
 
0.1%
0.05 93
 
0.1%
0.03 92
 
0.1%
0.04 83
 
0.1%
Other values (76) 893
 
0.7%
ValueCountFrequency (%)
0 88753
70.5%
0.01 6
 
< 0.1%
0.02 60
 
< 0.1%
0.03 92
 
0.1%
0.04 83
 
0.1%
0.05 93
 
0.1%
0.06 65
 
0.1%
0.07 67
 
0.1%
0.08 63
 
0.1%
0.09 44
 
< 0.1%
ValueCountFrequency (%)
1 34874
27.7%
0.96 1
 
< 0.1%
0.95 40
 
< 0.1%
0.94 13
 
< 0.1%
0.93 8
 
< 0.1%
0.92 12
 
< 0.1%
0.91 16
 
< 0.1%
0.9 10
 
< 0.1%
0.89 12
 
< 0.1%
0.88 11
 
< 0.1%

rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct82
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11995944
Minimum0
Maximum1
Zeros109782
Zeros (%)87.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:26.435428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.32043661
Coefficient of variation (CV)2.6712081
Kurtosis3.4457825
Mean0.11995944
Median Absolute Deviation (MAD)0
Skewness2.3255178
Sum15111.53
Variance0.10267962
MonotonicityNot monotonic
2023-06-14T10:25:26.683495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 109782
87.1%
1 12874
 
10.2%
0.9 269
 
0.2%
0.92 216
 
0.2%
0.93 210
 
0.2%
0.89 196
 
0.2%
0.91 187
 
0.1%
0.5 163
 
0.1%
0.88 141
 
0.1%
0.95 137
 
0.1%
Other values (72) 1797
 
1.4%
ValueCountFrequency (%)
0 109782
87.1%
0.01 61
 
< 0.1%
0.02 77
 
0.1%
0.03 99
 
0.1%
0.04 55
 
< 0.1%
0.05 37
 
< 0.1%
0.06 25
 
< 0.1%
0.07 23
 
< 0.1%
0.08 23
 
< 0.1%
0.09 10
 
< 0.1%
ValueCountFrequency (%)
1 12874
10.2%
0.99 23
 
< 0.1%
0.98 17
 
< 0.1%
0.97 32
 
< 0.1%
0.96 72
 
0.1%
0.95 137
 
0.1%
0.94 121
 
0.1%
0.93 210
 
0.2%
0.92 216
 
0.2%
0.91 187
 
0.1%

srv_rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12118423
Minimum0
Maximum1
Zeros109766
Zeros (%)87.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:27.091614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.32364833
Coefficient of variation (CV)2.6707132
Kurtosis3.4457696
Mean0.12118423
Median Absolute Deviation (MAD)0
Skewness2.327019
Sum15265.82
Variance0.10474824
MonotonicityNot monotonic
2023-06-14T10:25:27.348472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 109766
87.1%
1 14827
 
11.8%
0.5 244
 
0.2%
0.33 160
 
0.1%
0.25 114
 
0.1%
0.2 92
 
0.1%
0.17 73
 
0.1%
0.04 50
 
< 0.1%
0.03 47
 
< 0.1%
0.14 45
 
< 0.1%
Other values (52) 554
 
0.4%
ValueCountFrequency (%)
0 109766
87.1%
0.01 3
 
< 0.1%
0.02 40
 
< 0.1%
0.03 47
 
< 0.1%
0.04 50
 
< 0.1%
0.05 42
 
< 0.1%
0.06 33
 
< 0.1%
0.07 27
 
< 0.1%
0.08 40
 
< 0.1%
0.09 24
 
< 0.1%
ValueCountFrequency (%)
1 14827
11.8%
0.96 2
 
< 0.1%
0.95 1
 
< 0.1%
0.92 2
 
< 0.1%
0.9 1
 
< 0.1%
0.89 3
 
< 0.1%
0.88 4
 
< 0.1%
0.87 3
 
< 0.1%
0.86 5
 
< 0.1%
0.85 10
 
< 0.1%

same_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.66092497
Minimum0
Maximum1
Zeros2766
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:27.756552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.09
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.91

Descriptive statistics

Standard deviation0.43962357
Coefficient of variation (CV)0.66516411
Kurtosis-1.6097805
Mean0.66092497
Median Absolute Deviation (MAD)0
Skewness-0.57248654
Sum83258.04
Variance0.19326888
MonotonicityNot monotonic
2023-06-14T10:25:28.012627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 76811
61.0%
0.01 4027
 
3.2%
0.02 3616
 
2.9%
0.03 3503
 
2.8%
0.07 3438
 
2.7%
0.04 3227
 
2.6%
0.06 3220
 
2.6%
0.05 3088
 
2.5%
0.08 2815
 
2.2%
0 2766
 
2.2%
Other values (91) 19461
 
15.4%
ValueCountFrequency (%)
0 2766
2.2%
0.01 4027
3.2%
0.02 3616
2.9%
0.03 3503
2.8%
0.04 3227
2.6%
0.05 3088
2.5%
0.06 3220
2.6%
0.07 3438
2.7%
0.08 2815
2.2%
0.09 1957
1.6%
ValueCountFrequency (%)
1 76811
61.0%
0.99 759
 
0.6%
0.98 97
 
0.1%
0.97 44
 
< 0.1%
0.96 16
 
< 0.1%
0.95 14
 
< 0.1%
0.94 21
 
< 0.1%
0.93 33
 
< 0.1%
0.92 43
 
< 0.1%
0.91 25
 
< 0.1%

diff_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.063053139
Minimum0
Maximum1
Zeros76216
Zeros (%)60.5%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:28.244693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile0.29
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.18031504
Coefficient of variation (CV)2.8597313
Kurtosis18.899292
Mean0.063053139
Median Absolute Deviation (MAD)0
Skewness4.3797964
Sum7942.93
Variance0.032513512
MonotonicityNot monotonic
2023-06-14T10:25:28.565001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 76216
60.5%
0.06 18998
 
15.1%
0.07 9515
 
7.6%
0.05 6887
 
5.5%
1 3438
 
2.7%
0.08 1883
 
1.5%
0.01 1013
 
0.8%
0.09 645
 
0.5%
0.04 627
 
0.5%
0.5 549
 
0.4%
Other values (85) 6201
 
4.9%
ValueCountFrequency (%)
0 76216
60.5%
0.01 1013
 
0.8%
0.02 264
 
0.2%
0.03 282
 
0.2%
0.04 627
 
0.5%
0.05 6887
 
5.5%
0.06 18998
 
15.1%
0.07 9515
 
7.6%
0.08 1883
 
1.5%
0.09 645
 
0.5%
ValueCountFrequency (%)
1 3438
2.7%
0.99 39
 
< 0.1%
0.98 6
 
< 0.1%
0.97 7
 
< 0.1%
0.96 29
 
< 0.1%
0.95 39
 
< 0.1%
0.92 2
 
< 0.1%
0.91 1
 
< 0.1%
0.9 1
 
< 0.1%
0.89 1
 
< 0.1%

srv_diff_host_rate
Real number (ℝ)

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.097322421
Minimum0
Maximum1
Zeros97573
Zeros (%)77.5%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:28.941097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.25983138
Coefficient of variation (CV)2.6697999
Kurtosis6.816217
Mean0.097322421
Median Absolute Deviation (MAD)0
Skewness2.8603394
Sum12259.9
Variance0.067512348
MonotonicityNot monotonic
2023-06-14T10:25:29.157881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 97573
77.5%
1 8143
 
6.5%
0.01 2865
 
2.3%
0.5 982
 
0.8%
0.67 975
 
0.8%
0.12 904
 
0.7%
0.33 790
 
0.6%
0.02 771
 
0.6%
0.11 732
 
0.6%
0.25 724
 
0.6%
Other values (50) 11513
 
9.1%
ValueCountFrequency (%)
0 97573
77.5%
0.01 2865
 
2.3%
0.02 771
 
0.6%
0.03 218
 
0.2%
0.04 187
 
0.1%
0.05 325
 
0.3%
0.06 520
 
0.4%
0.07 519
 
0.4%
0.08 653
 
0.5%
0.09 618
 
0.5%
ValueCountFrequency (%)
1 8143
6.5%
0.88 1
 
< 0.1%
0.83 7
 
< 0.1%
0.8 60
 
< 0.1%
0.75 235
 
0.2%
0.71 9
 
< 0.1%
0.67 975
 
0.8%
0.62 7
 
< 0.1%
0.6 178
 
0.1%
0.57 33
 
< 0.1%

dst_host_count
Real number (ℝ)

Distinct256
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.1492
Minimum0
Maximum255
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:29.389923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q182
median255
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)173

Descriptive statistics

Standard deviation99.206565
Coefficient of variation (CV)0.54464453
Kurtosis-1.0657764
Mean182.1492
Median Absolute Deviation (MAD)0
Skewness-0.83344286
Sum22945699
Variance9841.9426
MonotonicityNot monotonic
2023-06-14T10:25:29.702038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 74099
58.8%
1 3119
 
2.5%
2 2733
 
2.2%
3 1280
 
1.0%
4 1198
 
1.0%
5 723
 
0.6%
6 701
 
0.6%
7 645
 
0.5%
8 595
 
0.5%
9 578
 
0.5%
Other values (246) 40301
32.0%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 3119
2.5%
2 2733
2.2%
3 1280
1.0%
4 1198
 
1.0%
5 723
 
0.6%
6 701
 
0.6%
7 645
 
0.5%
8 595
 
0.5%
9 578
 
0.5%
ValueCountFrequency (%)
255 74099
58.8%
254 70
 
0.1%
253 89
 
0.1%
252 77
 
0.1%
251 90
 
0.1%
250 93
 
0.1%
249 78
 
0.1%
248 87
 
0.1%
247 89
 
0.1%
246 83
 
0.1%

dst_host_srv_count
Real number (ℝ)

Distinct256
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.65372
Minimum0
Maximum255
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:29.951870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median63
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)245

Descriptive statistics

Standard deviation110.70289
Coefficient of variation (CV)0.95719257
Kurtosis-1.7563425
Mean115.65372
Median Absolute Deviation (MAD)61
Skewness0.28370718
Sum14569131
Variance12255.129
MonotonicityNot monotonic
2023-06-14T10:25:30.168111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 35993
28.6%
1 8449
 
6.7%
2 5161
 
4.1%
3 2768
 
2.2%
4 2488
 
2.0%
5 2336
 
1.9%
20 2300
 
1.8%
254 2238
 
1.8%
6 2222
 
1.8%
19 2190
 
1.7%
Other values (246) 59827
47.5%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 8449
6.7%
2 5161
4.1%
3 2768
 
2.2%
4 2488
 
2.0%
5 2336
 
1.9%
6 2222
 
1.8%
7 2160
 
1.7%
8 2072
 
1.6%
9 1948
 
1.5%
ValueCountFrequency (%)
255 35993
28.6%
254 2238
 
1.8%
253 472
 
0.4%
252 213
 
0.2%
251 402
 
0.3%
250 302
 
0.2%
249 248
 
0.2%
248 205
 
0.2%
247 220
 
0.2%
246 196
 
0.2%

dst_host_same_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52124448
Minimum0
Maximum1
Zeros6927
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:30.408251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.05
median0.51
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation0.44895005
Coefficient of variation (CV)0.86130419
Kurtosis-1.8840466
Mean0.52124448
Median Absolute Deviation (MAD)0.49
Skewness-0.010462886
Sum65662.21
Variance0.20155615
MonotonicityNot monotonic
2023-06-14T10:25:30.632706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 49059
38.9%
0.01 7780
 
6.2%
0 6927
 
5.5%
0.02 6593
 
5.2%
0.07 5672
 
4.5%
0.04 5208
 
4.1%
0.05 4951
 
3.9%
0.03 4049
 
3.2%
0.06 3444
 
2.7%
0.08 2816
 
2.2%
Other values (91) 29473
23.4%
ValueCountFrequency (%)
0 6927
5.5%
0.01 7780
6.2%
0.02 6593
5.2%
0.03 4049
3.2%
0.04 5208
4.1%
0.05 4951
3.9%
0.06 3444
2.7%
0.07 5672
4.5%
0.08 2816
 
2.2%
0.09 1740
 
1.4%
ValueCountFrequency (%)
1 49059
38.9%
0.99 688
 
0.5%
0.98 821
 
0.7%
0.97 478
 
0.4%
0.96 675
 
0.5%
0.95 580
 
0.5%
0.94 393
 
0.3%
0.93 457
 
0.4%
0.92 341
 
0.3%
0.91 395
 
0.3%

dst_host_diff_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.082951529
Minimum0
Maximum1
Zeros46989
Zeros (%)37.3%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:30.904372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.02
Q30.07
95-th percentile0.56
Maximum1
Range1
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.18892249
Coefficient of variation (CV)2.2775046
Kurtosis12.634273
Mean0.082951529
Median Absolute Deviation (MAD)0.02
Skewness3.6095829
Sum10449.57
Variance0.035691708
MonotonicityNot monotonic
2023-06-14T10:25:31.161692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46989
37.3%
0.07 16570
 
13.2%
0.06 9787
 
7.8%
0.01 9295
 
7.4%
0.05 7321
 
5.8%
0.08 7001
 
5.6%
0.02 6716
 
5.3%
0.03 3562
 
2.8%
0.04 3091
 
2.5%
0.09 2569
 
2.0%
Other values (91) 13071
 
10.4%
ValueCountFrequency (%)
0 46989
37.3%
0.01 9295
 
7.4%
0.02 6716
 
5.3%
0.03 3562
 
2.8%
0.04 3091
 
2.5%
0.05 7321
 
5.8%
0.06 9787
 
7.8%
0.07 16570
 
13.2%
0.08 7001
 
5.6%
0.09 2569
 
2.0%
ValueCountFrequency (%)
1 2139
1.7%
0.99 31
 
< 0.1%
0.98 35
 
< 0.1%
0.97 86
 
0.1%
0.96 63
 
0.1%
0.95 87
 
0.1%
0.94 45
 
< 0.1%
0.93 54
 
< 0.1%
0.92 40
 
< 0.1%
0.91 86
 
0.1%

dst_host_same_src_port_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14837869
Minimum0
Maximum1
Zeros63023
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:31.386533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.30899835
Coefficient of variation (CV)2.0824982
Kurtosis2.7623614
Mean0.14837869
Median Absolute Deviation (MAD)0
Skewness2.0870329
Sum18691.56
Variance0.095479981
MonotonicityNot monotonic
2023-06-14T10:25:31.594676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63023
50.0%
0.01 17657
 
14.0%
1 10307
 
8.2%
0.02 5743
 
4.6%
0.03 3278
 
2.6%
0.04 2096
 
1.7%
0.05 1664
 
1.3%
0.06 1299
 
1.0%
0.08 1086
 
0.9%
0.5 1077
 
0.9%
Other values (91) 18742
 
14.9%
ValueCountFrequency (%)
0 63023
50.0%
0.01 17657
 
14.0%
0.02 5743
 
4.6%
0.03 3278
 
2.6%
0.04 2096
 
1.7%
0.05 1664
 
1.3%
0.06 1299
 
1.0%
0.07 1051
 
0.8%
0.08 1086
 
0.9%
0.09 712
 
0.6%
ValueCountFrequency (%)
1 10307
8.2%
0.99 139
 
0.1%
0.98 192
 
0.2%
0.97 145
 
0.1%
0.96 229
 
0.2%
0.95 220
 
0.2%
0.94 113
 
0.1%
0.93 159
 
0.1%
0.92 124
 
0.1%
0.91 149
 
0.1%

dst_host_srv_diff_host_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.032542708
Minimum0
Maximum1
Zeros86903
Zeros (%)69.0%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:31.819446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.02
95-th percentile0.18
Maximum1
Range1
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.11256421
Coefficient of variation (CV)3.4589689
Kurtosis35.772928
Mean0.032542708
Median Absolute Deviation (MAD)0
Skewness5.5481513
Sum4099.47
Variance0.012670702
MonotonicityNot monotonic
2023-06-14T10:25:32.075511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 86903
69.0%
0.02 7952
 
6.3%
0.01 7146
 
5.7%
0.03 4723
 
3.7%
0.04 4518
 
3.6%
0.05 3048
 
2.4%
0.5 1550
 
1.2%
0.06 1330
 
1.1%
0.07 1036
 
0.8%
0.25 951
 
0.8%
Other values (65) 6815
 
5.4%
ValueCountFrequency (%)
0 86903
69.0%
0.01 7146
 
5.7%
0.02 7952
 
6.3%
0.03 4723
 
3.7%
0.04 4518
 
3.6%
0.05 3048
 
2.4%
0.06 1330
 
1.1%
0.07 1036
 
0.8%
0.08 488
 
0.4%
0.09 414
 
0.3%
ValueCountFrequency (%)
1 691
0.5%
0.97 2
 
< 0.1%
0.93 1
 
< 0.1%
0.88 1
 
< 0.1%
0.86 2
 
< 0.1%
0.83 2
 
< 0.1%
0.8 4
 
< 0.1%
0.78 1
 
< 0.1%
0.75 17
 
< 0.1%
0.73 2
 
< 0.1%

dst_host_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28445472
Minimum0
Maximum1
Zeros81385
Zeros (%)64.6%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:32.291567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.44478509
Coefficient of variation (CV)1.5636411
Kurtosis-1.0470193
Mean0.28445472
Median Absolute Deviation (MAD)0
Skewness0.96594003
Sum35833.33
Variance0.19783378
MonotonicityNot monotonic
2023-06-14T10:25:32.507617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81385
64.6%
1 33562
26.6%
0.01 3345
 
2.7%
0.02 1158
 
0.9%
0.03 711
 
0.6%
0.09 419
 
0.3%
0.08 413
 
0.3%
0.04 372
 
0.3%
0.99 304
 
0.2%
0.05 298
 
0.2%
Other values (91) 4005
 
3.2%
ValueCountFrequency (%)
0 81385
64.6%
0.01 3345
 
2.7%
0.02 1158
 
0.9%
0.03 711
 
0.6%
0.04 372
 
0.3%
0.05 298
 
0.2%
0.06 174
 
0.1%
0.07 197
 
0.2%
0.08 413
 
0.3%
0.09 419
 
0.3%
ValueCountFrequency (%)
1 33562
26.6%
0.99 304
 
0.2%
0.98 169
 
0.1%
0.97 100
 
0.1%
0.96 102
 
0.1%
0.95 71
 
0.1%
0.94 87
 
0.1%
0.93 76
 
0.1%
0.92 53
 
< 0.1%
0.91 48
 
< 0.1%

dst_host_srv_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27848673
Minimum0
Maximum1
Zeros85359
Zeros (%)67.8%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:32.795692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4456702
Coefficient of variation (CV)1.6003283
Kurtosis-1.0080163
Mean0.27848673
Median Absolute Deviation (MAD)0
Skewness0.99172136
Sum35081.53
Variance0.19862193
MonotonicityNot monotonic
2023-06-14T10:25:33.123777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 85359
67.8%
1 34256
27.2%
0.01 3762
 
3.0%
0.02 640
 
0.5%
0.03 160
 
0.1%
0.04 111
 
0.1%
0.5 107
 
0.1%
0.05 76
 
0.1%
0.08 72
 
0.1%
0.07 71
 
0.1%
Other values (90) 1358
 
1.1%
ValueCountFrequency (%)
0 85359
67.8%
0.01 3762
 
3.0%
0.02 640
 
0.5%
0.03 160
 
0.1%
0.04 111
 
0.1%
0.05 76
 
0.1%
0.06 53
 
< 0.1%
0.07 71
 
0.1%
0.08 72
 
0.1%
0.09 57
 
< 0.1%
ValueCountFrequency (%)
1 34256
27.2%
0.98 53
 
< 0.1%
0.97 56
 
< 0.1%
0.96 44
 
< 0.1%
0.95 26
 
< 0.1%
0.94 22
 
< 0.1%
0.93 20
 
< 0.1%
0.92 26
 
< 0.1%
0.91 20
 
< 0.1%
0.9 15
 
< 0.1%

dst_host_rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11883236
Minimum0
Maximum1
Zeros103178
Zeros (%)81.9%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:33.364530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.30655861
Coefficient of variation (CV)2.579757
Kurtosis3.6926841
Mean0.11883236
Median Absolute Deviation (MAD)0
Skewness2.3474327
Sum14969.55
Variance0.093978183
MonotonicityNot monotonic
2023-06-14T10:25:33.580608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 103178
81.9%
1 10298
 
8.2%
0.01 1800
 
1.4%
0.02 1222
 
1.0%
0.03 497
 
0.4%
0.05 408
 
0.3%
0.04 397
 
0.3%
0.91 267
 
0.2%
0.92 257
 
0.2%
0.89 244
 
0.2%
Other values (91) 7404
 
5.9%
ValueCountFrequency (%)
0 103178
81.9%
0.01 1800
 
1.4%
0.02 1222
 
1.0%
0.03 497
 
0.4%
0.04 397
 
0.3%
0.05 408
 
0.3%
0.06 220
 
0.2%
0.07 164
 
0.1%
0.08 147
 
0.1%
0.09 104
 
0.1%
ValueCountFrequency (%)
1 10298
8.2%
0.99 52
 
< 0.1%
0.98 68
 
0.1%
0.97 106
 
0.1%
0.96 168
 
0.1%
0.95 123
 
0.1%
0.94 135
 
0.1%
0.93 111
 
0.1%
0.92 257
 
0.2%
0.91 267
 
0.2%

dst_host_srv_rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12024085
Minimum0
Maximum1
Zeros106615
Zeros (%)84.6%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:33.826933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.31946048
Coefficient of variation (CV)2.6568382
Kurtosis3.5205967
Mean0.12024085
Median Absolute Deviation (MAD)0
Skewness2.3379124
Sum15146.98
Variance0.102055
MonotonicityNot monotonic
2023-06-14T10:25:34.035926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 106615
84.6%
1 13231
 
10.5%
0.01 1390
 
1.1%
0.02 580
 
0.5%
0.03 352
 
0.3%
0.05 351
 
0.3%
0.04 344
 
0.3%
0.98 189
 
0.2%
0.99 188
 
0.1%
0.06 185
 
0.1%
Other values (91) 2547
 
2.0%
ValueCountFrequency (%)
0 106615
84.6%
0.01 1390
 
1.1%
0.02 580
 
0.5%
0.03 352
 
0.3%
0.04 344
 
0.3%
0.05 351
 
0.3%
0.06 185
 
0.1%
0.07 97
 
0.1%
0.08 66
 
0.1%
0.09 39
 
< 0.1%
ValueCountFrequency (%)
1 13231
10.5%
0.99 188
 
0.1%
0.98 189
 
0.2%
0.97 103
 
0.1%
0.96 78
 
0.1%
0.95 73
 
0.1%
0.94 75
 
0.1%
0.93 50
 
< 0.1%
0.92 38
 
< 0.1%
0.91 51
 
< 0.1%

attack
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
normal
67342 
neptune
41214 
satan
 
3633
ipsweep
 
3599
portsweep
 
2931
Other values (18)
7253 

Length

Max length15
Median length6
Mean length6.3869908
Min length3

Characters and Unicode

Total characters804582
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rowneptune
3rd rownormal
4th rownormal
5th rowneptune

Common Values

ValueCountFrequency (%)
normal 67342
53.5%
neptune 41214
32.7%
satan 3633
 
2.9%
ipsweep 3599
 
2.9%
portsweep 2931
 
2.3%
smurf 2646
 
2.1%
nmap 1493
 
1.2%
back 956
 
0.8%
teardrop 892
 
0.7%
warezclient 890
 
0.7%
Other values (13) 376
 
0.3%

Length

2023-06-14T10:25:34.436067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
normal 67342
53.5%
neptune 41214
32.7%
satan 3633
 
2.9%
ipsweep 3599
 
2.9%
portsweep 2931
 
2.3%
smurf 2646
 
2.1%
nmap 1493
 
1.2%
back 956
 
0.8%
teardrop 892
 
0.7%
warezclient 890
 
0.7%
Other values (13) 376
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n 155804
19.4%
e 98333
12.2%
a 78970
9.8%
r 75714
9.4%
m 71528
8.9%
o 71471
8.9%
l 68308
8.5%
p 56948
 
7.1%
t 49623
 
6.2%
u 43959
 
5.5%
Other values (14) 33924
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 804491
> 99.9%
Connector Punctuation 91
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 155804
19.4%
e 98333
12.2%
a 78970
9.8%
r 75714
9.4%
m 71528
8.9%
o 71471
8.9%
l 68308
8.5%
p 56948
 
7.1%
t 49623
 
6.2%
u 43959
 
5.5%
Other values (13) 33833
 
4.2%
Connector Punctuation
ValueCountFrequency (%)
_ 91
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 804491
> 99.9%
Common 91
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 155804
19.4%
e 98333
12.2%
a 78970
9.8%
r 75714
9.4%
m 71528
8.9%
o 71471
8.9%
l 68308
8.5%
p 56948
 
7.1%
t 49623
 
6.2%
u 43959
 
5.5%
Other values (13) 33833
 
4.2%
Common
ValueCountFrequency (%)
_ 91
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 804582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 155804
19.4%
e 98333
12.2%
a 78970
9.8%
r 75714
9.4%
m 71528
8.9%
o 71471
8.9%
l 68308
8.5%
p 56948
 
7.1%
t 49623
 
6.2%
u 43959
 
5.5%
Other values (14) 33924
 
4.2%

last_flag
Real number (ℝ)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.504056
Minimum0
Maximum21
Zeros66
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-06-14T10:25:34.779882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q118
median20
Q321
95-th percentile21
Maximum21
Range21
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2915116
Coefficient of variation (CV)0.11748898
Kurtosis13.369134
Mean19.504056
Median Absolute Deviation (MAD)1
Skewness-2.8967599
Sum2456965
Variance5.2510255
MonotonicityNot monotonic
2023-06-14T10:25:35.011943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
21 62557
49.7%
18 20667
 
16.4%
20 19338
 
15.4%
19 10284
 
8.2%
15 3990
 
3.2%
17 3074
 
2.4%
16 2393
 
1.9%
12 729
 
0.6%
14 674
 
0.5%
11 641
 
0.5%
Other values (12) 1625
 
1.3%
ValueCountFrequency (%)
0 66
 
0.1%
1 62
 
< 0.1%
2 54
 
< 0.1%
3 65
 
0.1%
4 79
0.1%
5 81
0.1%
6 96
0.1%
7 118
0.1%
8 106
0.1%
9 194
0.2%
ValueCountFrequency (%)
21 62557
49.7%
20 19338
 
15.4%
19 10284
 
8.2%
18 20667
 
16.4%
17 3074
 
2.4%
16 2393
 
1.9%
15 3990
 
3.2%
14 674
 
0.5%
13 451
 
0.4%
12 729
 
0.6%

attack_class
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0.0
67342 
1.0
45927 
2.0
11656 
3.0
 
995
4.0
 
52

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters377916
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 67342
53.5%
1.0 45927
36.5%
2.0 11656
 
9.3%
3.0 995
 
0.8%
4.0 52
 
< 0.1%

Length

2023-06-14T10:25:35.292425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-14T10:25:35.540064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 67342
53.5%
1.0 45927
36.5%
2.0 11656
 
9.3%
3.0 995
 
0.8%
4.0 52
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 193314
51.2%
. 125972
33.3%
1 45927
 
12.2%
2 11656
 
3.1%
3 995
 
0.3%
4 52
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 251944
66.7%
Other Punctuation 125972
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 193314
76.7%
1 45927
 
18.2%
2 11656
 
4.6%
3 995
 
0.4%
4 52
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 125972
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 377916
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 193314
51.2%
. 125972
33.3%
1 45927
 
12.2%
2 11656
 
3.1%
3 995
 
0.3%
4 52
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 377916
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 193314
51.2%
. 125972
33.3%
1 45927
 
12.2%
2 11656
 
3.1%
3 995
 
0.3%
4 52
 
< 0.1%

Interactions

2023-06-14T10:25:02.612608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:21:50.230446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:21:56.180608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:02.709823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:09.491666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:16.594249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:23.965976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:30.641426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:37.876770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:44.297966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:51.378684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:57.987279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:04.934270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:12.109406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:18.788279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:25.795433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:32.953358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:40.538356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:47.816838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:54.248212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:00.468815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:07.967391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:15.548205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:22.057787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:28.775125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:34.772693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:41.309287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:48.324358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:55.986388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:25:02.933377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:21:50.430652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:21:56.379288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:02.909897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:09.823627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:16.794318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:24.174059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:30.889494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:38.108810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:44.481988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:51.546737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:58.267308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:05.174359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:12.352280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:19.076360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:26.059482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:33.233409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:40.738857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:47.976857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:54.488704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:00.668854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:08.407501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:15.812483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:22.313855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:28.967194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:34.940743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:41.589384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:48.581543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:56.154441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:25:03.165440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:21:50.625643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:21:56.603357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:03.133947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:10.231709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:16.986346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:24.390316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:31.113572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:38.397481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:44.778911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:51.730958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:58.459378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:05.422425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:12.616269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:19.380412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:26.331583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:33.441501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:40.994432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:48.181718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:54.712763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:00.900924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:08.615559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:16.048157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:22.570181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:29.183255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:35.260908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:41.885467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:48.845616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:56.419380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:25:03.469519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:21:50.833695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:21:56.827412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:03.358799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:10.456318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:17.226402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:24.622149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:31.377618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:38.589551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:45.026973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:51.922811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:58.771461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:05.622478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:12.848327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:19.556483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:26.579617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:33.753549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:41.331714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:48.405936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:55.008842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:01.197986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:08.839616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:16.256513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:22.873979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-06-14T10:23:24.354001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:31.721020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:39.390994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:46.543890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:53.123495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:59.435393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:06.606823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:14.659997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:20.984457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:27.566831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:33.843287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:40.068199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:47.261223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:54.845906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:25:01.462293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:25:08.518711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:21:55.421595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:01.821599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:08.427389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:15.793648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:23.010959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:29.705214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:36.930743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:43.441741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:50.450456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:56.899245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:03.960400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:11.211389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:17.819244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:24.562021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:32.049132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:39.647120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:46.824190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:53.350147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:59.635473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:06.871127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:14.828047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:21.176498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:27.862896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:34.019327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:40.372266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:47.558847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:55.110998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:25:01.643397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:25:08.774806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:21:55.605661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:02.022071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:08.755497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:15.994087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:23.229790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:29.945247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:37.170789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:43.601788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:50.626494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:57.235044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:04.152467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:11.411937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:18.067333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:24.850480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:32.305459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:39.898154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:47.152050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:53.574222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:59.819521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:07.079191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:14.996080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:21.352538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:28.094980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:34.192097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:40.612309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:47.751103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:55.343240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:25:01.828178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:25:09.047745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:21:55.812538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:02.301739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:09.011535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:16.186135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:23.478108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:30.153631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:37.436635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:43.881834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:50.898565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:57.491114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:04.368518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:11.628825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:18.339120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:25.130184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:32.521234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:40.114224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:47.401000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:53.784073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:00.011542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:07.407267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:15.196130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:21.608582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:28.270995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:34.408581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:40.869192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:47.940264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:55.535136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:25:02.052230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:25:09.271548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:21:56.004560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:02.509780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:09.219613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:16.394215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:23.677902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:30.449395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:37.653173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:44.081889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:51.218652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:22:57.699164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:04.672601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:11.893168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:18.523187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:25.450551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:32.721277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:40.386276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:47.576980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:23:54.008329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:00.236765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:07.647331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:15.380161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:21.833748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:28.519078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:34.584242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:41.093232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:48.116288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:24:55.753771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-14T10:25:02.404321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-06-14T10:25:37.196869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
durationsrc_bytesdst_byteshotnum_failed_loginsnum_compromisednum_rootnum_file_creationsnum_access_filescountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_ratelast_flagprotocol_typeserviceflaglandwrong_fragmenturgentlogged_inroot_shellsu_attemptednum_shellsis_host_loginis_guest_loginattackattack_class
duration1.0000.2260.1490.2290.0580.0800.0420.0870.048-0.324-0.319-0.185-0.1820.0510.0460.169-0.1400.016-0.067-0.156-0.1400.1960.182-0.027-0.156-0.1530.0650.069-0.0080.0820.1600.1850.0000.0000.0090.0650.1630.1820.0000.0000.0190.1640.125
src_bytes0.2261.0000.7000.2040.0130.1550.0810.0730.063-0.524-0.053-0.674-0.653-0.363-0.3440.753-0.7050.289-0.4050.6210.617-0.5250.3770.344-0.624-0.609-0.230-0.2590.2800.0000.0070.0900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.008
dst_bytes0.1490.7001.0000.2000.0250.171-0.0130.0400.068-0.440-0.017-0.536-0.510-0.300-0.2780.630-0.6080.310-0.3420.7080.667-0.6250.0590.327-0.519-0.483-0.219-0.1860.4600.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.009
hot0.2290.2040.2001.0000.0930.5150.0090.0530.002-0.153-0.143-0.086-0.083-0.0090.0120.107-0.104-0.028-0.0690.0050.063-0.060-0.002-0.066-0.064-0.0700.0950.095-0.1520.0260.2750.0260.0000.0000.0000.0990.0460.0120.0150.0000.8210.1540.177
num_failed_logins0.0580.0130.0250.0931.0000.0350.0370.0630.003-0.038-0.040-0.018-0.0180.0310.0300.023-0.022-0.017-0.031-0.0190.007-0.0030.015-0.0180.0070.0080.0300.029-0.0490.0080.0790.0520.0000.0000.2110.0150.0440.1050.0000.0000.0150.3130.080
num_compromised0.0800.1550.1710.5150.0351.0000.1650.1120.093-0.087-0.079-0.062-0.060-0.0020.0270.076-0.075-0.014-0.0470.0070.069-0.068-0.002-0.053-0.031-0.0280.1250.148-0.1530.0000.0310.0290.0000.0000.0860.0100.2430.2910.0000.0000.0000.0000.000
num_root0.0420.081-0.0130.0090.0370.1651.0000.1110.101-0.080-0.080-0.043-0.041-0.025-0.0260.050-0.044-0.027-0.054-0.031-0.0210.0390.0610.035-0.032-0.023-0.011-0.013-0.0020.0000.0390.0290.0000.0000.0710.0130.2880.3440.0000.0000.0000.0000.002
num_file_creations0.0870.0730.0400.0530.0630.1120.1111.0000.062-0.052-0.051-0.022-0.024-0.015-0.0150.031-0.029-0.008-0.021-0.021-0.0100.0190.0160.002-0.001-0.0070.0010.002-0.0160.0000.0540.0330.0000.0000.0860.0290.1110.1640.0760.0000.0040.0220.009
num_access_files0.0480.0630.0680.0020.0030.0930.1010.0621.000-0.054-0.044-0.032-0.030-0.019-0.0080.040-0.0390.009-0.0000.0120.0110.002-0.015-0.012-0.023-0.023-0.010-0.0070.0210.0160.0400.0150.0000.0000.0170.0670.4070.4660.0260.0000.0000.0520.024
count-0.324-0.524-0.440-0.153-0.038-0.087-0.080-0.052-0.0541.0000.5190.5780.5420.0710.065-0.7200.617-0.3250.620-0.325-0.4290.362-0.546-0.5310.5360.5050.0220.024-0.1530.2810.3820.2540.0060.1100.0000.6490.0280.0120.0070.0000.0790.3610.398
srv_count-0.319-0.053-0.017-0.143-0.040-0.079-0.080-0.051-0.0440.5191.0000.0730.108-0.208-0.2080.032-0.0390.2350.2180.3000.257-0.241-0.197-0.1220.0460.080-0.213-0.220-0.0530.5470.3600.0830.0000.2680.0000.2290.0070.0000.0000.0000.0290.3010.116
serror_rate-0.185-0.674-0.536-0.086-0.018-0.062-0.043-0.022-0.0320.5780.0731.0000.973-0.174-0.179-0.7550.674-0.3250.430-0.523-0.5730.485-0.485-0.3890.9360.922-0.227-0.206-0.1620.2170.2460.3910.0230.1380.0000.4940.0170.0000.0030.0000.0610.3110.403
srv_serror_rate-0.182-0.653-0.510-0.083-0.018-0.060-0.041-0.024-0.0300.5420.1080.9731.000-0.222-0.237-0.7060.625-0.3050.411-0.479-0.5280.439-0.471-0.3690.9190.942-0.276-0.259-0.1380.2150.2370.3770.0210.0410.0000.4960.0160.0000.0020.0000.0600.3100.397
rerror_rate0.051-0.363-0.300-0.0090.031-0.002-0.025-0.015-0.0190.071-0.208-0.174-0.2221.0000.966-0.2230.234-0.1490.083-0.312-0.2920.288-0.014-0.078-0.179-0.2240.8390.884-0.1500.1270.1470.3700.0240.0240.0000.2900.0120.0000.0000.0000.0350.2500.210
srv_rerror_rate0.046-0.344-0.2780.0120.0300.027-0.026-0.015-0.0080.065-0.208-0.179-0.2370.9661.000-0.2100.213-0.1210.075-0.297-0.2750.273-0.020-0.075-0.187-0.2380.8310.894-0.1410.1280.1120.3610.0000.0240.0000.2930.0060.0000.0000.0000.0350.2110.180
same_srv_rate0.1690.7530.6300.1070.0230.0760.0500.0310.040-0.7200.032-0.755-0.706-0.223-0.2101.000-0.9200.385-0.5410.6990.758-0.6510.5250.488-0.717-0.678-0.144-0.1570.1770.2110.3200.2960.0060.0450.0000.6060.0270.0110.0070.0000.0720.3210.419
diff_srv_rate-0.140-0.705-0.608-0.104-0.022-0.075-0.044-0.029-0.0390.617-0.0390.6740.6250.2340.213-0.9201.000-0.3760.526-0.668-0.7270.646-0.443-0.4820.6450.5970.1560.160-0.2110.1190.1600.1460.0000.0160.0000.1740.0000.0000.0000.0000.0170.2240.202
srv_diff_host_rate0.0160.2890.310-0.028-0.017-0.014-0.027-0.0080.009-0.3250.235-0.325-0.305-0.149-0.1210.385-0.3761.000-0.3110.3960.442-0.4040.1600.342-0.327-0.301-0.136-0.1250.1380.2780.2580.1090.0410.1070.0000.3350.0110.0000.0000.0000.0400.2120.221
dst_host_count-0.067-0.405-0.342-0.069-0.031-0.047-0.054-0.021-0.0000.6200.2180.4300.4110.0830.075-0.5410.526-0.3111.000-0.350-0.5320.435-0.693-0.8380.4230.3800.0520.030-0.1270.2290.2430.1610.0330.0520.0050.4640.0300.0110.0150.0000.0740.2320.278
dst_host_srv_count-0.1560.6210.7080.005-0.0190.007-0.031-0.0210.012-0.3250.300-0.523-0.479-0.312-0.2970.699-0.6680.396-0.3501.0000.919-0.8410.1520.448-0.528-0.469-0.265-0.2490.3820.2530.4050.2520.0140.1190.0000.6490.0080.0160.0220.0000.1630.2960.386
dst_host_same_srv_rate-0.1400.6170.6670.0630.0070.069-0.021-0.0100.011-0.4290.257-0.573-0.528-0.292-0.2750.758-0.7270.442-0.5320.9191.000-0.8990.3050.539-0.582-0.515-0.247-0.2200.2710.2190.4150.2660.0100.1350.0040.6300.0270.0360.0250.0000.2410.2980.379
dst_host_diff_srv_rate0.196-0.525-0.625-0.060-0.003-0.0680.0390.0190.0020.362-0.2410.4850.4390.2880.273-0.6510.646-0.4040.435-0.841-0.8991.000-0.213-0.4900.5050.4340.2680.224-0.2600.1730.2120.2180.0070.0750.0110.1760.0100.0160.0000.0270.0300.2760.294
dst_host_same_src_port_rate0.1820.3770.059-0.0020.015-0.0020.0610.016-0.015-0.546-0.197-0.485-0.471-0.014-0.0200.525-0.4430.160-0.6930.1520.305-0.2131.0000.561-0.455-0.4500.039-0.008-0.1240.4360.2870.1510.0370.1490.0040.2120.0060.0000.0310.0000.0390.2720.292
dst_host_srv_diff_host_rate-0.0270.3440.327-0.066-0.018-0.0530.0350.002-0.012-0.531-0.122-0.389-0.369-0.078-0.0750.488-0.4820.342-0.8380.4480.539-0.4900.5611.000-0.385-0.340-0.062-0.0380.2130.4390.2950.0890.1010.0540.1030.1440.0280.0200.0000.0000.0200.3570.289
dst_host_serror_rate-0.156-0.624-0.519-0.0640.007-0.031-0.032-0.001-0.0230.5360.0460.9360.919-0.179-0.187-0.7170.645-0.3270.423-0.528-0.5820.505-0.455-0.3851.0000.919-0.195-0.206-0.1750.2130.2500.3430.0230.0790.0000.4970.0280.0380.0160.0680.0620.3150.410
dst_host_srv_serror_rate-0.153-0.609-0.483-0.0700.008-0.028-0.023-0.007-0.0230.5050.0800.9220.942-0.224-0.238-0.6780.597-0.3010.380-0.469-0.5150.434-0.450-0.3400.9191.000-0.272-0.250-0.1160.2110.2550.3740.1010.0410.0000.4960.0680.0810.0530.0630.0610.2990.398
dst_host_rerror_rate0.065-0.230-0.2190.0950.0300.125-0.0110.001-0.0100.022-0.213-0.227-0.2760.8390.831-0.1440.156-0.1360.052-0.265-0.2470.2680.039-0.062-0.195-0.2721.0000.880-0.1910.1240.1460.3270.0000.1520.0000.2780.0170.0090.0060.0000.0330.2390.237
dst_host_srv_rerror_rate0.069-0.259-0.1860.0950.0290.148-0.0130.002-0.0070.024-0.220-0.206-0.2590.8840.894-0.1570.160-0.1250.030-0.249-0.2200.224-0.008-0.038-0.206-0.2500.8801.000-0.1350.1280.1500.3560.0000.0240.0000.2830.0790.0620.0000.0000.0510.1820.193
last_flag-0.0080.2800.460-0.152-0.049-0.153-0.002-0.0160.021-0.153-0.053-0.162-0.138-0.150-0.1410.177-0.2110.138-0.1270.3820.271-0.260-0.1240.213-0.175-0.116-0.191-0.1351.0000.3410.2410.1510.0780.1950.0390.3700.1190.0330.0610.0000.1880.3780.421
protocol_type0.0820.0000.0000.0260.0080.0000.0000.0000.0160.2810.5470.2170.2150.1270.1280.2110.1190.2780.2290.2530.2190.1730.4360.4390.2130.2110.1240.1280.3411.0000.9230.2780.0050.1920.0000.3850.0170.0070.0050.0000.0460.6640.319
service0.1600.0070.0000.2750.0790.0310.0390.0540.0400.3820.3600.2460.2370.1470.1120.3200.1600.2580.2430.4050.4150.2120.2870.2950.2500.2550.1460.1500.2410.9231.0000.2980.1110.2160.0250.8690.1610.1250.0480.0000.8200.3550.557
flag0.1850.0900.0230.0260.0520.0290.0290.0330.0150.2540.0830.3910.3770.3700.3610.2960.1460.1090.1610.2520.2660.2180.1510.0890.3430.3740.3270.3560.1510.2780.2981.0000.0210.0540.0000.6530.0530.0510.0070.0000.0790.4350.490
land0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0230.0210.0240.0000.0060.0000.0410.0330.0140.0100.0070.0370.1010.0230.1010.0000.0000.0780.0050.1110.0211.0000.0000.0000.0100.0000.0000.0000.0000.0000.8480.009
wrong_fragment0.0000.0000.0000.0000.0000.0000.0000.0000.0000.1100.2680.1380.0410.0240.0240.0450.0160.1070.0520.1190.1350.0750.1490.0540.0790.0410.1520.0240.1950.1920.2160.0540.0001.0000.0000.0760.0000.0000.0000.0000.0080.9840.087
urgent0.0090.0000.0000.0000.2110.0860.0710.0860.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0040.0110.0040.1030.0000.0000.0000.0000.0390.0000.0250.0000.0000.0001.0000.0070.1120.0980.0000.0000.0000.1690.038
logged_in0.0650.0000.0000.0990.0150.0100.0130.0290.0670.6490.2290.4940.4960.2900.2930.6060.1740.3350.4640.6490.6300.1760.2120.1440.4970.4960.2780.2830.3700.3850.8690.6530.0100.0760.0071.0000.0450.0310.0240.0000.1200.7330.710
root_shell0.1630.0000.0000.0460.0440.2430.2880.1110.4070.0280.0070.0170.0160.0120.0060.0270.0000.0110.0300.0080.0270.0100.0060.0280.0280.0680.0170.0790.1190.0170.1610.0530.0000.0000.1120.0451.0000.6100.1450.0000.0000.3420.278
su_attempted0.1820.0000.0000.0120.1050.2910.3440.1640.4660.0120.0000.0000.0000.0000.0000.0110.0000.0000.0110.0160.0360.0160.0000.0200.0380.0810.0090.0620.0330.0070.1250.0510.0000.0000.0980.0310.6101.0000.0230.0000.0000.1100.016
num_shells0.0000.0000.0000.0150.0000.0000.0000.0760.0260.0070.0000.0030.0020.0000.0000.0070.0000.0000.0150.0220.0250.0000.0310.0000.0160.0530.0060.0000.0610.0050.0480.0070.0000.0000.0000.0240.1450.0231.0000.0000.0000.3190.100
is_host_login0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0680.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
is_guest_login0.0190.0000.0000.8210.0150.0000.0000.0040.0000.0790.0290.0610.0600.0350.0350.0720.0170.0400.0740.1630.2410.0300.0390.0200.0620.0610.0330.0510.1880.0460.8200.0790.0000.0080.0000.1200.0000.0000.0000.0001.0000.3010.290
attack0.1640.0130.0180.1540.3130.0000.0000.0220.0520.3610.3010.3110.3100.2500.2110.3210.2240.2120.2320.2960.2980.2760.2720.3570.3150.2990.2390.1820.3780.6640.3550.4350.8480.9840.1690.7330.3420.1100.3190.0000.3011.0001.000
attack_class0.1250.0080.0090.1770.0800.0000.0020.0090.0240.3980.1160.4030.3970.2100.1800.4190.2020.2210.2780.3860.3790.2940.2920.2890.4100.3980.2370.1930.4210.3190.5570.4900.0090.0870.0380.7100.2780.0160.1000.0000.2901.0001.000

Missing values

2023-06-14T10:25:09.823681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-14T10:25:11.232141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateattacklast_flagattack_class
00udpotherSF146000000000000000001310.00.00.00.00.080.150.0025510.000.600.880.000.000.000.00.00normal150.0
10tcpprivateS000000000000000000012361.01.00.00.00.050.070.00255260.100.050.000.001.001.000.00.00neptune191.0
20tcphttpSF23281530000010000000000550.20.20.00.01.000.000.00302551.000.000.030.040.030.010.00.01normal210.0
30tcphttpSF199420000001000000000030320.00.00.00.01.000.000.092552551.000.000.000.000.000.000.00.00normal210.0
40tcpprivateREJ000000000000000000121190.00.01.01.00.160.060.00255190.070.070.000.000.000.001.01.00neptune211.0
50tcpprivateS000000000000000000016691.01.00.00.00.050.060.0025590.040.050.000.001.001.000.00.00neptune211.0
60tcpprivateS0000000000000000000117161.01.00.00.00.140.060.00255150.060.070.000.001.001.000.00.00neptune211.0
70tcpremote_jobS0000000000000000000270231.01.00.00.00.090.050.00255230.090.050.000.001.001.000.00.00neptune211.0
80tcpprivateS000000000000000000013381.01.00.00.00.060.060.00255130.050.060.000.001.001.000.00.00neptune211.0
90tcpprivateREJ000000000000000000205120.00.01.01.00.060.060.00255120.050.070.000.000.000.001.01.00neptune211.0
durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateattacklast_flagattack_class
1259620tcphttpSF33416000000010000000000330.000.000.00.01.000.000.002552551.000.000.000.000.000.00.000.0normal210.0
1259630tcpprivateS000000000000000000012891.001.000.00.00.070.050.00255120.050.060.000.001.001.00.000.0neptune211.0
1259640tcpsmtpSF22333650000010000000000110.000.000.00.01.000.000.00121.000.001.001.000.000.00.000.0normal190.0
1259650tcpprivateS000000000000000000011331.001.000.00.00.030.070.00255130.050.070.000.001.001.00.000.0neptune211.0
1259660tcphttpSF35937500000100000000003110.330.090.00.01.000.000.1832551.000.000.330.040.330.00.000.0normal180.0
1259670tcpprivateS0000000000000000000184251.001.000.00.00.140.060.00255250.100.060.000.001.001.00.000.0neptune201.0
1259688udpprivateSF1051450000000000000000220.000.000.00.01.000.000.002552440.960.010.010.000.000.00.000.0normal210.0
1259690tcpsmtpSF22313840000010000000000110.000.000.00.01.000.000.00255300.120.060.000.000.720.00.010.0normal180.0
1259700tcpkloginS000000000000000000014481.001.000.00.00.060.050.0025580.030.050.000.001.001.00.000.0neptune201.0
1259710tcpftp_dataSF15100000010000000000110.000.000.00.01.000.000.00255770.300.030.300.000.000.00.000.0normal210.0